Search Results (35)

This work focuses on studying the magnetohydrodynamic flow and heat transfer mechanism of a Casson–Maxwell nanofluid due to a stretching surface through a porous medium, using a physics-informed neural network (PINNs) approach as the main tool for the solution of the physical problem. The mathematical model describes the phenomena of viscosity variation with temperature, viscous dissipation, thermal slip, Brownian motion, thermophoresis, and drag force due to a porous medium, which give a realistic physical scenario of the coupled transport phenomena of momentum, heat, and nanoparticles. First, the nonlinear partial differential equations are converted into a dimensionless boundary layer model using similarity transformations. Then, the yielded system is solved via the PINNs approach, which integrates physical law within the optimization procedure. The proposed technique does not require a significant number of labeled datasets and provides accurate and stable predictions of the strongly nonlinear flow. A comprehensive parametric analysis was performed to explore the impact of the dimensionless controlling factors on the velocity, temperature, and nanoparticle concentration distributions. It is found that the interaction of magnetic field effects, porous media resistivity, thermal and concentration slip, viscosity variation, and viscous heating significantly modifies the transport features for the studied model of the Casson–Maxwell nanofluid, which can be used effectively to control the rate of heat and mass transfer. This study proves the efficiency of the PINN technique in solving this type of model, and it also provides useful insights for designing thermal systems, energy conversion devices, and electrically conducting viscoelastic nanofluid transport problems. The close concordance between the present findings and established data from the literature validates the precision and dependability of the developed PINN-based framework. Full article

Spacecraft radiation assessment requires expert interpretation of continuous environment-model outputs. We present a reflection-equivariant Mamdani fuzzy system for relative triage of modelled total ionising dose (TID) and solar-proton exposure. Radiation environment severity and solar-proton severity are derived from OMERE 5.9.5 runs of the AE9/AP9 (IRENE 1.57.004, mean mode) and Emission of Solar Protons (ESP, 90 per cent confidence) models, and, together with mission duration, are mapped through reflection-paired membership partitions and a 27-rule sum-based rule base to four triage categories. We prove reflection symmetry of the input and output partitions, permutation symmetry of the rule map, risk-reversal duality of the aggregated inference and centroid score, and reflection equivariance of a normalised output-support vector retained before defuzzification. The architecture is examined on nine reference mission scenarios and additional boundary cases using sensitivity, comparative-variant and cumulative-versus-duration-normalised analyses. The results show exact algebraic consistency with the imposed symmetry identities and transparent rule-level traceability, while also revealing the small local non-monotonicity of the centroid score and formulation sensitivity in the seven-year GLONASS-like scenario. Under the integrated-exposure formulation, scores range from 0.381 for the polar low-Earth-orbit scenario to 0.892 for the geostationary orbit (GEO). Because the same nine scenarios also define the frozen normalisation anchors, this range is a reference-set demonstration rather than an out-of-sample result. Evaluation to date comprises internal mathematical-consistency checks, comparison with an author-defined conservative heuristic and concordance with a seven-member expert panel blinded to the model output but rating the same scenario descriptions; the system has not been validated against ground-truth radiation-hardness outcomes such as mission anomaly records or component-qualification results. Cases for which the integrated and duration-normalised diagnostics disagree are flagged for separate engineering analysis. The system is a reference-benchmarked proof-of-concept pre-screening method and does not replace project-specific TID, total non-ionising dose (TNID), single-event-effect, shielding or component-qualification analysis. Full article

Background and Objectives: Intra-cyclic variation in swimming velocity is one of the variables most commonly used to assess swimming performance, providing simple, straightforward information for coaches and swimmers. Therefore, the main aim of this study was to assess the consistency and association between the two most commonly used methods for calculating the intra-cyclic variation in velocity in swimming (coefficient of variation—dv1; and range: the difference between the maximum and minimum instantaneous velocities—dv2). Methods: Twenty-one young butterfly swimmers (11 males and 10 females) with an average age of 17.3 ± 2.8 years were recruited for analysis. The measurements were done in the butterfly stroke at maximal velocities. The consistency and association criteria included the intraclass correlation coefficient (ICC) and simple linear regression between the two methods. Results: The ICC between dvs was excellent for the overall sample (ICC = 0.972, p < 0.001), males (ICC = 0.978, p < 0.001), and females (ICC = 0.955, p < 0.001). The linear regression between methods was very high (overall: R² = 0.93, p < 0.001; males: R² = 0.91, p < 0.001; females: R² = 0.86, p < 0.001). Conclusions: Overall, coaches and researchers should be aware that although both methods yielded highly concordant results, they are based on different mathematical formulations and quantify distinct characteristics of the velocity signal. Therefore, their consistency and association should not be interpreted as mathematical equivalence or universal interchangeability. Full article

Objective diagnosis in psychiatry remains challenging due to the lack of reliable biological markers and the presence of confounding variables in observational data. While EEG-based machine learning models have shown promising classification performance, their validity remains unclear when confounding factors such as age are not explicitly controlled. In this work, we propose a confound-aware mathematical framework for supervised learning, where classification is formulated as a mapping $f:{\mathbb{R}}^{E\times C\times T}\to \mathcal{Y}$ under the presence of a confounding variable A. Within this formulation, model performance is interpreted as a function of both predictive structure and confound dependence. The proposed framework integrates classification, regression, and feature selection into a unified evaluation pipeline. A central contribution is the Cross-Task Explanation Concordance (CTEC) index, a rank-based metric that quantifies the stability of feature importance across models and predictive tasks. Experimental results on a large-scale EEG dataset (N = 670) demonstrate that deep learning models outperform handcrafted approaches under standard evaluation. However, under confound-controlled settings, handcrafted models show a dual response to confound control: age residualization improves classification by removing feature-level noise (+20.3%), while age-matching collapses performance to chance (balanced accuracy, BA = 0.238) by eliminating demographic separability. Deep learning models retain partial robustness under both conditions. These findings highlight that conventional performance metrics may overestimate model validity in the presence of structured bias. The proposed framework provides a general mathematical approach for evaluating supervised learning models under confounding effects and is applicable to a wide range of data-driven systems beyond EEG. Full article

Animal functional trait data are essential for macroecology, but massive datasets remain locked in unstructured scientific literature. Traditional manual extraction is inefficient, and general-purpose artificial intelligence (AI) systems struggle with complex biological tables and numerical accuracy. To address this bioinformatics challenge, we propose a multimodal neuro-symbolic framework combining visual-language perception and code-based reasoning. This approach reconstructs complex document layouts and delegates biostatistical calculations, such as unit normalization and thermodynamic energy conversion, to an isolated programming environment to ensure mathematical and statistical consistency. By mining literature spanning 117 years, we constructed a high-fidelity physiological database for 1632 chordate species. Our method achieved a macro-averaged F1 score of 0.935 in extracting biophysical fields. External benchmarking against a curated mammalian trait database showed strong concordance for shared body-mass and metabolic-rate traits, while our database retained record-level provenance and physiological context. Furthermore, the extracted data reproduced classic allometric scaling relationships for basal metabolic rate and brain volume while preserving physiological adaptations, supporting the biological plausibility of the dataset. This study validates a reproducible bioinformatics pipeline that minimizes extraction artifacts and substantially reduces downstream mathematical and statistical conversion errors, while providing a scalable, complementary resource for building physiology-oriented trait databases from historical literature. Full article

Inferring demographic history from whole-genome data is a central objective in evolutionary and conservation genomics. However, the Pairwise Sequentially Markovian Coalescent (PSMC) framework, one of the most widely used demographic inference methods for whole-genome sequence data, is highly sensitive to sequencing coverage, with low coverage producing systematic underestimation of heterozygosity, which biases effective population size trajectories. Here, we present PSMC-FAC, an automated method designed to optimize false-negative rate correction in low-coverage genomes by minimizing geometric distances between FNR-corrected low-coverage trajectories and their corresponding high-coverage references. Whole-genome datasets from humans, gray wolves, and cattle were downsampled across multiple coverage levels and processed through standard demographic inference pipelines. Corrected trajectories, projected onto a common temporal grid, were compared using Hausdorff and discrete Fréchet distance metrics and optimal correction factors were modeled as a function of sequencing depth using second-degree polynomial regression. Across species and demographic contexts, PSMC-FAC substantially improved concordance between low- and high-coverage trajectories and revealed highly predictable coverage-dependent correction patterns. Overall, PSMC-FAC provides a reproducible and mathematically grounded alternative to subjective correction approaches, enabling reliable demographic inference from moderate-coverage genomes and facilitating broader population-scale genomic analyses. Full article

Background: Chronic liver disease (CLD) in children requires long-term monitoring. Liver biopsy and transient elastography (TE) are resource-intensive methods that require specialized equipment and trained personnel. Simple indirect fibrosis scores based on routine laboratory parameters offer a potentially cost-effective alternative but have not been systematically evaluated in pediatric populations with diverse CLD etiologies. Objectives: This study aimed to assess the performance of several indirect fibrosis and cirrhosis scores in predicting significant (≥F2) and advanced (≥F3) fibrosis and cirrhosis (F4) in children with CLD using TE as a comparator. Methods: We retrospectively reviewed medical records of children with CLD evaluated at a tertiary center between January 2023 and June 2025. TE results and routine laboratory data were used to calculate fibrosis scores, including APRI, FIB-4, FibroIndex, FORNS, GPR, GUCI, King’s score, and Lok’s index. ROC analyses were performed to assess each score’s ability to discriminate significant fibrosis, advanced fibrosis and cirrhosis. Optimal cut-offs were established using the Youden index. Results: GPR showed the strongest concordance with TE-based fibrosis classification across both fibrosis thresholds, achieving an AUROC of 0.835 for significant fibrosis and a superior 0.917 for advanced fibrosis. FibroIndex and APRI also demonstrated good discriminatory power for advanced disease. Utilizing mathematically optimized cut-offs, GPR (0.45) and APRI (0.84) achieved good negative predictive values (100% and 95%) and sensitivities (100% and 85%) for advanced fibrosis, establishing them as potentially valuable screening tools. For cirrhosis detection (F4), Lok’s Index performed best (AUROC 0.854). Conclusions: In this diverse pediatric cohort, simple indirect scores—particularly GPR, APRI, and FibroIndex—demonstrated the highest concordance relative to TE findings, with negative predictive values up to 100% for GPR. This indicates that they can serve as reliable first-line screening tools when TE is unavailable. While their good negative predictive values allow for the confident exclusion of severe disease—potentially sparing many children from invasive testing—their low positive predictive values limit their role in definitive diagnosis. The systematic failure of adult-derived, age-dependent formulas in this cohort underscores the critical need for specialized pediatric biomarkers. Full article

In industrial combustion processes, high concentrations of propane (C₃H₈) gas are employed. Therefore, developing gas-detecting devices that operate under high concentrations, elevated temperatures, and short response times is crucial. This paper presents the design, simulation, and construction of a novel propane (C₃H₈) gas detector. The design was based on the dynamic electrical response of a gas sensor fabricated with cobalt antimoniate (CoSb₂O₆). The simulation considered the device structure and programming criteria, and the final prototype was constructed according to the sensor response, design parameters, and operating principles. Design, simulation, and fabrication results were in concordance, confirming the correct operation of the detector at high gas concentrations. A mathematical model was derived from the sensor’s electrical response, establishing a resistance value that allowed a two-second response time. This resistance was used to adapt the signal between the gas sensor and the PIC18F2550 microcontroller. Input/output signals, safety criteria, and functionality principles were considered in the programming device. The resulting propane (C₃H₈) gas detector operates at 300 °C, detects high C₃H₈ concentrations, and achieves a 2 s response time, making it ideal for industrial applications where combustion monitoring is essential. Full article

This study aimed to evaluate and compare the performance of five mathematical models: Gompertz, Ørskov & McDonald, Brody, Richards, and the Dual Pool Logistic model, in describing the in vitro gas production kinetics of Capiaçu elephant grass (Pennisetum purpureum Schum ‘BRS Capiaçu’) silages. The effect of including dehydrated cashew pseudo-fruit on the in vitro degradation curves was also assessed. A completely randomized design was adopted, using Capiaçu silages containing 0%, 10%, 20%, or 30% dehydrated cashew pseudo-fruit. Rumen fermentation kinetics were measured through cumulative in vitro gas production. Model performance was evaluated using the Akaike Information Criterion (AIC), coefficient of determination (R²), concordance correlation coefficient (CCC), and mean square prediction error (MSPE). Accuracy (pMSPE) and precision (AIC) were also considered. The Richards model performed best with the lowest AIC (1119.07) and MSPE (0.246) and the highest R² (0.917) and CCC (0.966). It was over 350 times more likely to provide a correct fit (p < 0.05) compared to the other models. Significant differences (p < 0.05) were observed between degradation curves as a function of the pseudo-fruit inclusion level. Increasing pseudo-fruit inclusion improved silage composition, raising total digestible nutrients (from 54.6% to 67.1%) and reducing neutral detergent fiber (from 58.5% to 42.3%), which directly enhanced fermentation kinetics. These results indicate that the Richards model is the most suitable for describing the fermentation kinetics of Capiaçu elephant grass silages. Moreover, linking model performance to practice, the Richards model provides a reliable tool for determining optimal inclusion levels of dehydrated cashew pseudo-fruit (up to 30%), supporting better silage nutritional quality and more efficient feed utilization in ruminant production systems. Full article

The circadian phase difference between morning and evening types is a fundamental aspect of chronotype. However, results of categorizations into chronotypes based on reported sleep times show low concordance with those based on measurements of the hormonal or physiological or molecular rhythm–markers of the circadian phase. This might be partially explained by the profound individual differences in the phase angle between the sleep–wake cycle and these rhythms that depends on chronotype, age, sex, and other factors. Here, we examined the possibility of using self-reported sleep times in the condition of 5-days-on/2-days-off school/work schedule to estimate circadian phase differences between various chronotypes. In an in silico study, we determined that, for such an estimation, similarities of the compared chronotypes in weekend sleep duration and weekend–weekday gap and in risetime are required. In the following empirical and simulation studies of sleep times reported by 4940 survey participants, we provided examples of the estimation of circadian differences between chronotypes, and the model-based simulations of sleep times in morning and evening types exemplified a way to confirm such estimations. The results of in silico, empirical, and simulation studies underscore the possibility of using bedtimes and risetimes for direct estimation of the circadian phase differences between individuals in real-life situations, such as a 5-days-on/2-days-off school/work schedule. Additionally, the results of these studies on different chronotypes provided further mathematical modeling and empirical evidence for our failure to sleep more on weekends to recover/compensate/pay back/ catch up on lost sleep. Full article

Concerns about mycotoxin contamination by Alternaria spp. in tomato-based products emphasize the need for understanding the effect of the environment on their production. In the current study, we focused on three species frequently associated with tomato (A. alternata, A. solani, and A. tenuissima) by evaluating the effects of different temperatures (5 to 40 °C) and substrata (PDA and V8) on mycelial growth and the production of mycotoxins (alternariol, alternariol monomethyl ether, and tenuazonic acid). Both biological processes were supported between 5 and 35 °C, with optimal temperatures between 20 and 30 °C, depending on the species. Temperature and its interaction with species significantly (p < 0.05) affected both processes. However, the species factor alone was not significant (p > 0.05), indicating that environmental conditions affect Alternaria spp. growth and mycotoxin production more than the species itself does. Mathematical equations were developed to describe the effect of temperature on mycelial growth, as well as on the production of AOH, AME, and TeA, for each Alternaria species. High concordance (CCC ≥ 0.807) between observed and predicted data and low levels of residual error (RMSE ≤ 0.147) indicated the high goodness of fit of the developed equations, which may be used for the development of models to predict Alternaria contamination both in field and during post-harvest storage. Full article

Understanding the structural concordance between taxonomic and functional diversity (FD) metrics is essential for improving the ecological interpretation of community patterns in biomonitoring programs. This study evaluated the concordance between taxonomic and FD metrics of benthic macroinvertebrates along a fluvial habitat quality gradient in the Paute River Basin, Ecuador. Macroinvertebrate communities were sampled over six years at twelve sampling points and assessed using four taxonomic metrics: Shannon diversity (H), the Margalef index (D_Mg), family richness (N), and the Andean Biotic Index (ABI). Functional diversity was evaluated using four metrics: weighted functional dendrogram-based diversity (wFDc), Rao’s quadratic entropy (Rao), functional dispersion (FDis), and functional richness (FRic). The fluvial habitat index (FHI) was used as an environmental reference to evaluate diversity metric responses. K-means clustering was independently applied to each metric, and pairwise concordance was quantified using the Measure of Concordance (MoC) and overlap in sampling points groupings across replicates. Most metrics (except FRic and N) showed clear responsiveness to the FHI gradient, confirming their ecological relevance. Strong structural concordance was observed between H and D_Mg and the FD metrics Rao, FDis, and wFDc, showing that these metrics captured similar yet complementary aspects of community organization. In contrast, ABI showed marked sensitivity to the FHI gradient but low concordance with functional metrics, suggesting distinct dimensions of biological integrity not encompassed by trait-based metrics. These findings highlight the value of combining taxonomic and functional metrics to detect both broad and subtle ecological changes. Integrating metrics with differing structural properties and environmental sensitivities can enhance the robustness of freshwater biomonitoring frameworks, especially in systems undergoing ecological transition or habitat degradation. Full article

In this paper, the prototype of a gas detector based on the electrical response of MgSb₂O₆ oxide at 400 °C and with a concentration of 560 ppm was designed, simulated, and fabricated. This design considers a PIC18F4550 microcontroller and a response time of 3 s for the sensor. It is worth noting that the response system can be reduced in concordance with the mathematical model of the sensor’s electrical response. The proposed device is capable of detecting one to three gases: O₂, C₃H₈, and CO₂. The configuration is achieved through three switches. In programming the prototype, factors such as the gas sensor signals, device configuration, corrective gas signals, and indicator signals were carefully considered. The characteristic of the gas detector is an operational temperature of 400 °C, which is ideal for industrial processing. This can be configured to detect a single gas or all three of them $\left({\mathrm{O}}_2,{\mathrm{C}}_3{\mathrm{H}}_8,\mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{C}\mathrm{O}}_2\right)$. Each gas type has its corresponding corrective signal and an indicator-led diode. The operation concentration is 560 ppm, the device is scalable, and its programming can be extended to cover industrial networks. Full article

In order to investigate the impact of two immunization strategies—vaccination targeting susceptible individuals to reduce their infection rate and clinical medical interventions targeting infected individuals to enhance their recovery rate—on the spread of infectious diseases in complex networks, this study proposes a bilinear SIR infectious disease model that considers bidirectional immunization. By analyzing the conditions for the existence of endemic equilibrium points, we derive the basic reproduction numbers and outbreak thresholds for both homogeneous and heterogeneous networks. The epidemic model is then reconstructed and extensively analyzed using continuous-time Markov chain (CTMC) methods. This analysis includes the investigation of transition probabilities, transition rate matrices, steady-state distributions, and the transition probability matrix based on the embedded chain. In numerical simulations, a notable concordance exists between the outcomes of CTMC and mean-field (MF) simulations, thereby substantiating the efficacy of the CTMC model. Moreover, the CTMC-based model adeptly captures the inherent stochastic fluctuation in the disease transmission, which is consistent with the mathematical properties of Markov chains. We further analyze the relationship between the system’s steady-state infection density and the immunization rate through MCS. The results suggest that the infection density decreases with an increase in the immunization rate among susceptible individuals. The current research results will enhance our understanding of infectious disease transmission patterns in real-world scenarios, providing valuable theoretical insights for the development of epidemic prevention and control strategies. Full article

The olive mill wastewater (OMW) treatment process is modeled and optimized through new design of experiments (DOE). The first step of the process is coagulation–flocculation using three coagulants (modeled with the mixture design) followed by photo-degradation (modelled with the full factorial design). Based on this methodology, we successfully established a direct correlation between the system’s composition during the coagulation–flocculation step and the conditions of the photo-catalytic degradation step. Three coagulants are used in this study, Fe³⁺ solution, lime, and cactus juice, and two parameters are considered for the photo-degradation conditions: dilution and catalyst mass. Utilizing a sophisticated quadratic model, the analysis of the two observed responses reveals the ideal parameters for achieving maximum efficiency in coagulation–flocculation and photo-degradation processes. This is attained using a quasi-equal mixture of limewater and cactus juice, exclusively. To achieve an optimal photo-catalytic degradation, it is essential to maintain a minimal dilution rate while employing an elevated concentration of TiO₂. It was found that the experimental tests validations were in good concordance with the mathematical predictions (a decolorization of 92.57 ± 0.90% and an organic degradation of 96.19 ± 0.97%). Full article